# Clinicogenomic Insights for Progression-Free Survival in Prostate Cancer

## TL;DR

This study combines clinical and genomic data to better predict prostate cancer progression risks, helping guide targeted treatment and monitoring.

## Contribution

The novel integration of clinicogenomic data improves progression-free survival modeling and identifies key clinical and genomic predictors.

## Key findings

- Survival models achieved high accuracy in ranking prostate cancer patients by progression risk.
- Clinical variables like treatment history and tumor recurrence were consistently important predictors.
- Genomic data, particularly the gene MYH6, provided additional insights into progression risks.

## Abstract

Public health relevance—How does this work relate to a public health issue?
Prostate cancer remains one of the most prevalent malignancies among men worldwide, with substantial morbidity, mortality, and healthcare burden, driven by heterogeneous disease progression patterns.This study leverages survival analysis models to identify patient profiles at elevated risk of prostate cancer progression, aiming to generate hypotheses for investigation that can support more targeted clinical intervention and patient monitoring.

Prostate cancer remains one of the most prevalent malignancies among men worldwide, with substantial morbidity, mortality, and healthcare burden, driven by heterogeneous disease progression patterns.

This study leverages survival analysis models to identify patient profiles at elevated risk of prostate cancer progression, aiming to generate hypotheses for investigation that can support more targeted clinical intervention and patient monitoring.

Public health significance—Why is this work of significance to public health?
This work revealed insights into prostate cancer by showing that key clinical factors remain primary drivers of progression risks, while genomic factors provide additional information on disease progression and potential biological mechanisms.This combined clinicogenomics perspective supports biologically informed risk stratification and facilitates hypothesis generation.

This work revealed insights into prostate cancer by showing that key clinical factors remain primary drivers of progression risks, while genomic factors provide additional information on disease progression and potential biological mechanisms.

This combined clinicogenomics perspective supports biologically informed risk stratification and facilitates hypothesis generation.

Public health implications—What are the key implications or messages for practitioners, policy makers, and/or researchers in public health?
Progression-free survival models can be used to help distinguish patients at higher risk of cancer progression from those with more stable conditions, potentially reducing overtreatment and focusing resources where they are most needed.The findings highlight the value of integrating genomic and clinical data for prostate cancer evaluation and monitoring, which offers a foundation for assessing progression risks, thereby enabling biologically informed cohort-level disease risk assessment and stratification.

Progression-free survival models can be used to help distinguish patients at higher risk of cancer progression from those with more stable conditions, potentially reducing overtreatment and focusing resources where they are most needed.

The findings highlight the value of integrating genomic and clinical data for prostate cancer evaluation and monitoring, which offers a foundation for assessing progression risks, thereby enabling biologically informed cohort-level disease risk assessment and stratification.

Prostate cancer (PrCa), the second most common cancer diagnosed in men globally, remains a critical challenge in precision oncology. While PrCa can be deadly, it is highly treatable if detected early. Identifying associative factors influencing disease progression risks can help inform preliminary steps that will further the expedition of clinical therapeutic intervention decisions, which will improve treatment outcomes. While conventional PrCa progression assessment tools rely heavily on a few clinical parameters, the importance of genomic information is increasingly recognized. In this study, we evaluate the prognostic value of patients’ clinicogenomic profiles in modeling progression-free survival (PFS) of PrCa. Three survival models, namely the penalized Cox model, random survival forest, and a deep learning survival neural network, were deployed with extensive tuning applied to a dataset for a cohort of 494 patients with PrCa. This dataset, compiled from public data in The Cancer Genome Atlas (TCGA) accessed via cBioPortal, consists of relevant clinical features and single-nucleotide variant information on likely PrCa-related genes. The survival models demonstrated satisfactory discriminatory performance, with Harrell’s concordance index ranging from approximately 0.80 to 0.87 on held-out test data, indicating their ability to rank patients according to their relative progression risk among patients, while exhibiting distinct dynamics, all three models consistently identified clinical variables that indicated neoadjuvant treatment history, neoplasm cancer status, and tumor recurrence as well as the gene MYH6 as important predictor variables for PrCa PFS. Our findings suggest the incorporation of genomic data into the survival modeling workflow, thereby allowing the use of integrated clinicogenomics information to gain insights into progression risks for patients with PrCa.